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Machine Learning is a science that enables machines (especially computers) to learn from environments and make own decisions.
Research Activities
- The Machine Learning Laboratory (MLL) carries out research and develops different theoretical foundations for machine learning such as:
- Reinforcement Learning, Deep Learning, Statistical Learning Theory, Multi-agent Systems, Game Theory and Mechanism Design, Blockchains, Explainable and Fair AI
- How machines and multi-agent systems should help in planning activities by learning from environments
- How learning gets affected if different machine learning and multi-agent systems algorithms are trying compete instead of cooperating
- How machines should learn in the presence of noisy environment or partial supervision
- Role of deep learning in planning, reinforcement learning, AI in game theory, blockchains and its applications
- For more details: Research Projects
Recent News
- Our paper “We might walk together, but I run faster: Network Fairness and Scalability in Blockchains” received the Best Poster Design Award at AAMAS 2021
- Our paper “Ballooning Multi-armed Bandits” got accepted in AI Journal
- One of our papers get accepted in Privacy Preserving AI (PPAI@AAAI21)
- Two of our papers get accepted in AAMAS 2021
- One of our papers get accepted in CODS-COMAD 2021
- One of our papers get accepted in PAKDD 2021
- Two of our papers get accepted in the WINE Workshop on Game Theory in Blockchain 2020
- MLL student Sankarshan Damle banged the prestigious Ripple-IIITH PhD fellowship for pursuing PhD in blockchain related areas.
- Two of our papers got accepted in ACML 2020
- Our lab members secure PhD admits with coveted research groups around the world: Susobhan Ghosh with the EconCS group at Harvard, Moin Moti with HKUST, and Rohith Gangam Reddy with UCI